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In this paper we compare two non-interferometric wavefront sensors suitable for in-situ high-NA EUV optical testing. The first is the AIS sensor, which has been deployed in both inspection and exposure tools. AIS is a compact, optical test that directly measures a wavefront by probing various parts of the imaging optic pupil and measuring localized wavefront curvature. The second is an image-based technique that uses an iterative algorithm based on simulated annealing to reconstruct a wavefront based on matching aerial images through focus. In this technique, customized illumination is used to probe the pupil at specific points to optimize differences in aberration signatures.

Figure error would degrade the performance of optical system. When predicting the performance and performing system assembly, compensation by clocking of optical components around the optical axis is a conventional but user-dependent method. Commercial optical software cannot optimize this clocking. Meanwhile existing automatic figure-error balancing methods can introduce approximate calculation error and the build process of optimization model is complex and time-consuming. To overcome these limitations, an accurate and automatic global optimization method of figure error balancing is proposed. This method is based on precise ray tracing to calculate the wavefront error, not approximate calculation, under a given elements’ rotation angles combination. The composite wavefront error root-mean-square (RMS) acts as the cost function. Simulated annealing algorithm is used to seek the optimal combination of rotation angles of each optical element. This method can be applied to all rotational symmetric optics. Optimization results show that this method is 49% better than previous approximate analytical method.

Fourier ptychographic microscopy (FPM) is a newly developed super-resolution technique, which employs angularly varying illuminations and a phase retrieval algorithm to surpass the diffraction limit of a low numerical aperture (NA) objective lens. In current FPM imaging platforms, accurate knowledge of LED matrix’s position is critical to achieve good recovery quality. Furthermore, considering such a wide field-of-view (FOV) in FPM, different regions in the FOV have different sensitivity of LED positional misalignment. In this work, we introduce an iterative method to correct position errors based on the simulated annealing (SA) algorithm. To improve the efficiency of this correcting process, large number of iterations for several images with low illumination NAs are firstly implemented to estimate the initial values of the global positional misalignment model through non-linear regression. Simulation and experimental results are presented to evaluate the performance of the proposed method and it is demonstrated that this method can both improve the quality of the recovered object image and relax the LED elements’ position accuracy requirement while aligning the FPM imaging platforms.

A highly efficient reflective 1×3 splitting grating with triangular structure operating in 1.064μm wavelength under normal incidence for TE polarization is designed. The schematic of the grating has four layers. The first layer with SiO2 is triangular structure. Rigorous coupled wave analysis (RCWA) and Simulated Annealing (SA) algorithm are adopted to design and analyze the properties. The theoretical efficiency is nearly about 99%. The bigger error tolerance is also analyzed by rigorous coupled wave analysis. These reflective gratings as splitters should be useful optical elements in the field of high-power laser as well as other reflective applications.

The ever increasing pattern densities and design complexities make the tuning of optical proximity correction (OPC) recipes more challenging. There are various recipe tuning methods to meet the challenge, such as genetic algorithm (GA), simulated annealing, and OPC software vendor provided recipe optimizers. However, these methodologies usually only consider edge placement errors (EPEs). Therefore, these techniques may not provide adequate freedom to solve unique problems at special geometries, for example bridge, pinch, and process variation band related violations at complex 2D geometries. This paper introduces a general methodology to fix specific problems identified at the OPC verification stage and demonstrates its successful application to two test-cases. The algorithm and method of the automatic scoring system is introduced in order to identify and prioritize the problems that need to be fixed based on severity, with the POR recipe score used as the baseline reference. A GA optimizer, whose objective function is based on the scoring system, is applied to tune the OPC recipe parameters to optimum condition after generations of selections. The GA optimized recipe would be compared to existing recipe to quantify the amount of improvement. This technique was subsequently applied to eliminate certain chronic OPC verification problems which were encountered in the past. Though the benefits have been demonstrated for limited test cases, employing this technique more universally will enable users to efficiently reduce the number of OPC verification violations and provide robust OPC solutions.

In this paper, we have addressed the issue of over-segmented regions produced in watershed by merging the regions using global feature. The global feature information is obtained from clustering the image in its feature space using Fuzzy C-Means (FCM) clustering. The over-segmented regions produced by performing watershed on the gradient of the image are then mapped to this global information in the feature space. Further to this, the global feature information is optimized using Simulated Annealing (SA). The optimal global feature information is used to derive the similarity criterion to merge the over-segmented watershed regions which are represented by the region adjacency graph (RAG). The proposed method has been tested on digital brain phantom simulated dataset to segment white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) soft tissues regions. The experiments showed that the proposed method performs statistically better, with average of 95.242% regions are merged, than the immersion watershed and average accuracy improvement of 8.850% in comparison with RAG-based immersion watershed merging using global and local features.

Tunable Diode Laser Absorption Tomography(TDLAT), as a promising technique which combines Tunable Diode Laser Absorption Spectroscopy(TDLAS) and computer tomography, has shown the advantages of high spatial resolution for temperature measurement. Given the large number of tomography algorithms, it is necessary to understand the feature of tomography algorithms and find suitable ones for the specific experiment. This paper illustrates two different algorithms including algebraic reconstruction technique (ART) and simulated annealing (SA) which are implemented using Matlab. The reconstruction simulations of unimodal and bimodal temperature phantom were done under different conditions, and the results of the simulation were analyzed. It shows that for the unimodal temperature phantom, the both algorithms work well, the reconstruction quality is acceptable under suitable conditions and the result of ART is better. But for the bimodal temperature phantom, the result of SA is much better. More specifically, the reconstruction quality of ART is mainly affected by the ray coverage, the maximum deviation for the unimodal temperature phantom is 5.9%, while for the bimodal temperature field, it is up to 25%. The reconstruction quality of SA is mainly affected by the number of the transitions, the maximum deviation for the unimodal temperature phantom is 9.2% when 6 transitions are used which is a little worse than the result of ART; however, the maximum deviation for the bimodal temperature phantom is much better than ART’s, which is about 5.2% when 6 transitions are used.

Many kinds of optimization algorithm have been applied to design diffractive optical elements (DOEs) for beam shaping. However, only the selected sampling points are controlled by these optimization algorithms, the intensity distribution of other points on the output plane is always far away from the ideal distribution. In our previous research, the non-selected points were well controlled by using a hybrid algorithm merging hill-climbing with simulated annealing, but this hybrid algorithm is time-consuming. In this paper, a new hybrid algorithm merging Gerchberg-Saxton algorithm with gradient method is presented. Because of the use of iterative algorithm, the optimization time is largely reduced. The intensity distribution of the non-selected points as well as that of the selected points is well controlled, and good performance of beam shaping is obtained. Finally the experimental results demonstrate the good performance of this algorithm.

Fuel cost accounts for 40 percent of the operating cost of an airline. Fuel cost can be minimized by planning a flight on optimized routes. The routes can be optimized by searching best connections based on the cost function defined by the airline. The most common algorithm that used to optimize route search is Dijkstra’s. Dijkstra’s algorithm produces a static result and the time taken for the search is relatively long. This paper experiments a new algorithm to optimize route search which combines the principle of simulated annealing and genetic algorithm. The experimental results of route search, presented are shown to be computationally fast and accurate compared with timings from generic algorithm. The new algorithm is optimal for random routing feature that is highly sought by many regional operators.

The growth control singularity model suggests that acupuncture points (acupoints) originate from organizers in embryogenesis. Organizers are singular points in growth control. Acupuncture can cause perturbation of a system with effects similar to simulated annealing. In clinical trial, the goal of a treatment is to relieve certain disorder which corresponds to reaching certain local optimum in simulated annealing. The self-organizing effect of the system is limited and related to the person’s general health and age. Perturbation at acupoints can lead a stronger local excitation (analogous to higher annealing temperature) compared to perturbation at non-singular points (placebo control points). Such difference diminishes as the number of perturbed points increases due to the wider distribution of the limited self-organizing activity. This model explains the following facts from systematic reviews of acupuncture trials: 1. Properly chosen single acupoint treatment for certain disorder can lead to highly repeatable efficacy above placebo 2. When multiple acupoints are used, the result can be highly repeatable if the patients are relatively healthy and young but are usually mixed if the patients are old, frail and have multiple disorders at the same time as the number of local optima or comorbidities increases. 3. As number of acupoints used increases, the efficacy difference between sham and real acupuncture often diminishes. It predicted that the efficacy of acupuncture is negatively correlated to the disease chronicity, severity and patient’s age. This is the first biological – physical model of acupuncture which can predict and guide clinical acupuncture research.

There is a growing need to characterize unknown foundations and assess substructures in existing bridges. It is becoming an important issue for the serviceability and safety of bridges as well as for the possibility of partial reuse of existing infrastructures. Within this broader contest, this paper investigates the possibility of identifying, locating and quantifying changes of boundary conditions, by leveraging a simply supported grid structure with a composite deck. Multi-reference impact tests are operated for the grid model and modification of one supporting bearing is done by replacing a steel cylindrical roller with a roller of compliant material. Impact based modal analysis provide global modal parameters such as damped natural frequencies, mode shapes and flexibility matrix that are used as indicators of boundary condition changes. An updating process combining a hybrid optimization algorithm and the finite element software suit ABAQUS is presented in this paper. The updated ABAQUS model of the grid that simulates the supporting bearing with springs is used to detect and quantify the change of the boundary conditions.

A simulated annealing method for material parameter extraction with terahertz time-domain spectroscopy is introduced to improve the common extraction method. The simulated annealing method seeks the global minimum of the error function to obtain material constants where the characterized material is not constrained by a certain boundary condition. It is shown from the research results that more accurate material parameters can be acquired to meet the actual requirements of material analysis by use of the simulated annealing method.

A 3×3 high divergent 2D-grating with period of 3.842μm at wavelength of 850nm under normal incidence is designed and fabricated in this paper. This high divergent 2D-grating is designed by the vector theory. The Rigorous Coupled Wave Analysis (RCWA) in association with the simulated annealing (SA) is adopted to calculate and optimize this 2D-grating.The properties of this grating are also investigated by the RCWA. The diffraction angles are more than 10 degrees in the whole wavelength band, which are bigger than the traditional 2D-grating. In addition, the small period of grating increases the difficulties of fabrication. So we fabricate the 2D-gratings by direct laser writing (DLW) instead of traditional manufacturing method. Then the method of ICP etching is used to obtain the high divergent 2D-grating.

In this paper we propose a method to get fine registration of high resolution multispectral images. The algorithm supposes that a coarse registration, based on ancillary information, has been already performed. It is known, in fact, that residual distortions remain, due to the combined effects of Earth rotation and curvature, view geometry, sensor operation, variations in platform velocity, atmospheric and terrain effects. The algorithm grounds its main idea on the information-theoretic approach to register volumetric medical images of different modalities. Registration is achieved by adjustment of the relative position and orientation until the mutual information between the images is maximized. The idea is that the join information is maximized when the two images are at their best registration. This approach works directly with image data but in principle it can be applied in any transformed domain. While the original algorithm has been thought to make registration in a limited search space (i.e. translation and orientation), in the remote sensing framework the class of transformations is extended allowing scaling, shearing or a general polynomial model. The maximization of the target function is performed using both the stochastic gradient descent algorithm and the simulated annealing, since the former is known to occasionally deadlock in local maxima. We have applied the algorithm on a SPOT-5 couple of images, achieving the registration of chips of size 256x256 pixels at time. Accuracy has been obtained comparing the results with the outcomes of a commercial software that adopts a sort of Normalized Cross-Correlation method. On 143 chips taken throughout the image, the final translation accuracy resulted well below 1 pixel and the rotation accuracy about 0.015deg.

We propose novel models for image restoration based on statistical physics. We investigate the affinity between these fields and describe a framework from which interesting denoising algorithms can be derived: Ising-like models and simulated annealing techniques. When combined with known predictors such as Median and LOCO-I, these models become even more effective. In order to further examine the proposed models we apply them to two important problems: (i) Digital Cameras in space damaged from cosmic radiation. (ii) Ultrasonic medical devices damaged from speckle noise. The results, as well as benchmark and comparisons, suggest in most of the cases a significant gain in PSNR and SSIM in comparison to other filters.

As lithography still pushing toward to lower k􀬵 imaging, traditional illumination source shapes may perform marginally in resolving complex layouts, freeform source shapes are expected to achieve better image quality. Illumination optimization as one of inverse lithography techniques attempts to synthesize the input source which leads to the desired output wafer pattern by inverting the forward model from mask to wafer. This paper proposes a method to optimize illumination by using simulated annealing algorithms (SA). A synthesis of the NILS values at multi-critical mask locations over a focus range is chose as the merit function. The advantage of the SA algorithm is that it can identify optimum source solutions without any additional apriori knowledge about lithographic processes. The results show that our method can provide great improvements in both image quality and DOF.

A hybrid algorithm based on the simulated annealing algorithm and the iterative algorithm is proposed for the design of diffractive optical element (DOE) to shape the laser beams. The algorithm has the global optimization ability of simulated annealing algorithm as well as the local optimization ability of iterative algorithm. Comparisons between the hybrid algorithm and other two optimization algorithms show that the hybrid algorithm has satisfactory convergence property and design accuracy. Numerical simulation results demonstrate that the diffraction efficiency of the DOE is higher than 94% and non-uniformity is less than 1%. Therefore, this algorithm can be well applied in the field of beam shaping.

We present Simulated Annealing fiber-to-target allocation simulations for the proposed DESI and 4MOST massively multiplexed spectroscopic surveys, and for both Poisson and realistically clustered mock target samples. We simulate both Echidna and theta-phi actuator designs, including the restrictions caused by the physical actuator characteristics during repositioning. For DESI, with theta-phi actuators, used in 5 passes over the sky for a mock ELG/LRG/QSO sample, with matched fiber and target densities, a total target allocation yield of 89.3% was achieved, but only 83.7% for the high-priority Ly-alpha QSOs. If Echidna actuators are used with the same pitch and number of passes, the yield increases by 5.7% and 16% respectively. Echidna also allows a factor-of-two increase in the number of close Ly-alpha QSO pairs that can be observed. Echidna spine tilt causes a variable loss of throughput, with average loss being the same as the loss at the rms tilt. With a natural tilt minimization scheme, we find an rms tilt always close to 0.58 x maximum. There is an additional but much smaller defocus loss, equivalent to an average defocus of 30microns. These tilt losses offset the gains in yield for Echidna, but because the survey strategy is driven by the higher priority targets, a clear survey speed advantage remains. For 4MOST, high and low latitude sample mock catalogs were supplied by the 4MOST team, and allocations were carried out with the proposed Echidna-based positioner geometry. At high latitudes, the resulting target completeness was 85.3% for LR targets and 78.9% for HR targets. At low latitude, the target completeness was 93.9% for LR targets and 71.2% for HR targets., 10 pages, SPIE 2014 9150-78

An improved phase retrieval algorithm for optical anti-counterfeiting mask is proposed which combines the G-S algorithm with the simulated annealing algorithm. The proposed algorithm takes full advantage of the respective advantages of the G-S algorithm and the simulated annealing algorithm. In the scheme, the redesigned simulated annealing algorithm is used to control the times of iterations to achieve global searching, which makes the G-S algorithm to jump out automatically when the program is trapped in local optimal solution. The computer simulation results show that the proposed algorithm improves the quality of decoded image obviously compared with the traditional G-S algorithm, and reduces the running time of the program greatly.

The objective of this paper is to present a systematic development of the generic shape optimization of elec- trostatically actuated microcantilever beams for extending their static travel range. Electrostatic actuators are widely used in micro electro mechanical system (MEMS) devices because of low power density and ease of fab- rication. However, their useful travel range is often restricted by a phenomenon known as pull-in instability. The Rayleigh- Ritz energy method is used for computation of pull-in parameters which includes electrostatic potential and fringing field effect. Appropriate width function and linear thickness functions are employed along the length of the non-prismatic beam to achieve enhanced travel range. Parameters used for varying the thick- ness and width functions are optimized using simulated annealing with pattern search method towards the end to refine the results. Appropriate penalties are imposed on the violation of volume, width, thickness and area constraints. Nine test cases are considered for demonstration of the said optimization method. Our results indicate that around 26% increase in the travel range of a non-prismatic beam can be achieved after optimiza- tion compared to that in a prismatic beam having the same volume. Our results also show an improvement in the pull-in displacement of around 5% compared to that of a variable width constant thickness actuator. We show that simulated annealing is an effective and flexible method to carry out design optimization of structural elements under electrostatic loading.

In this thesis, the currently feedback control algorithms used in the polarization controller, including simulated annealing algorithm and gradient algorithm were analyzed. On this basis, a new method of polarization control feedback algorithm based on fast locating algorithm was proposed to solve the defects of the original algorithm, such as poor convergence and long time consuming search. It can reduce convergence time and improve the response speed of the polarization controllers. This new endless polarization control algorithm using 4-plate polarization controller was proposed and demonstrated. The results showed that the response time of the polarization controller was less than 1ms. The control of polarization was achieved and the output polarization state was stable while the light intensity fluctuated less than 2%, which could run endless reset freely.

The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.

This paper presents a concealed target detection based on the intersection kernel Support Vector Machine (SVM). Hyperspectral imagers are widely used in the field of target detection and material analysis. In military applications, it can be used to border protection, concealed target detection, reconnaissance and surveillance. If disguised enemies not detected in advance, the damage of allies will be catastrophic by unexpected attack. Concealed object detection using radar and terahertz method is widely used. However, these active techniques are easily exposed to the enemy. Electronic Optical Counter Counter Measures (EOCCM) using hyperspectral imagers can be a feasible solution. We use the band selected feature directly and the intersection kernel based SVM. Different materials show different spectrums although they look similar in CCD camera. We propose novel concealed target detection method that consist of 4 step, Feature band selection, Feature Extraction, SVM learning and target detection.

It is practical and crucial that a designer will want to control the positions of some modules along the chip boundary in the final packing for I/O connection. To solve the problem we propose an algorithm named Area Estimate Transitive Closure Graphs (AE-TCG). By analyzing the feasible condition of boundary constraints, AE-TCG guarantees that the result of each perturbation is a feasible placement with boundary constraints, and doesn’t need to transform the infeasible solution to feasible one. Unlike most of the previous algorithms getting the target area after packing, AE-TCG can satisfy the boundary constraints and estimate the area of feasible placement without packing after random perturbation, then accept the beneficial perturbation. For the property of concentrate itself, AE-TCG is running without Simulated Annealing (SA) process. The experimental results show that AE-TCG is effective and efficient than other algorithms with boundary constraints in commonly used MCNC benchmark circuits.

This note describes a non-rigid image registration approach that parametrizes the deformation field by an additive composition of a similarity transformation and a set of Gaussian radial basis functions. The bases’ centers, variances, and weights are determined with a global optimization approach that is introduced in this work. This approach consists of simulated annealing with a particle filter based generator function to perform the optimization. Additionally, a local refinement is performed to capture the remaining misalignment. The deformation is constrained to be physically meaningful (i.e., invertible). Results on 2D and 3D data sets demonstrate the algorithm’s robustness to large deformations.

Compared with the conventional monitoring approach of separately sensing and then compressing the data, compressive sensing (CS) is a novel data acquisition framework whereby the compression is done during the sampling. If the original sensed signal would have been sufficiently sparse in terms of some orthogonal basis, the decompression can be done essentially perfectly up to some critical compression ratio. In structural health monitoring (SHM) systems for civil structures, novel data compression techniques such as CS are needed to reduce the cost of signal transfer and storage. In this article, Bayesian compressive sensing (BCS) is investigated for SHM signals. By explicitly quantifying the uncertainty in the signal reconstruction, the BCS technique exhibits an obvious benefit over the existing regularized norm-minimization CS. However, current BCS algorithms suffer from a robustness problem; sometimes the reconstruction errors are large. The source of the problem is that inversion of the compressed signal is a severely ill-posed problem that often leads to sub-optimal signal representations. To ensure the strong robustness of the signal reconstruction, even at a high compression ratio, an improved BCS algorithm is proposed which uses stochastic optimization for the automatic relevance determination approach to reconstructing the underlying signal. Numerical experiments are used as examples; the improved BCS algorithm demonstrates superior performance than state-of-the-art BCS reconstruction algorithms.

The transfer matrix method, based on plane wave theory, of multi-layer equivalent fluid is employed to evaluate the sound absorbing properties of two-layer-assembled and three-layer-assembled sintered fibrous sheets (generally regarded as a kind of compound absorber or structures). Two objective functions which are more suitable for the optimization of sound absorption properties of multi-layer absorbers within the wider frequency ranges are developed and the optimized results of using two objective functions are also compared with each other. It is found that using the two objective functions, especially the second one, may be more helpful to exert the sound absorbing properties of absorbers at lower frequencies to the best of their abilities. Then the calculation and optimization of sound absorption properties of multi-layer-assembled structures are performed by developing a simulated annealing genetic arithmetic program and using above-mentioned objective functions. Finally, based on the optimization in this work the thoughts of the gradient design over the acoustic parameters- the porosity, the tortuosity, the viscous and thermal characteristic lengths and the thickness of each samples- of porous metals are put forth and thereby some useful design criteria upon the acoustic parameters of each layer of porous fibrous metals are given while applying the multi-layer-assembled compound absorbers in noise control engineering.

The acoustical parameters of porous materials such as tortuosity factor, viscous and thermal characteristic length, and static flow resistivity, are very important, but not obtained directly from the conventional experimental measurements. Hence, in this study, the methods based on the Johnson-Champoux-Allard model and including the simulated annealing generic algorithm and successive quadratic programming are put forward so as to determine the acoustical parameters of a kind of sintered fibrous metal. And the corresponding results are compared to each other. It is found that the determination of the acoustical parameters of the sintered fibrous metals employing the simulated annealing generic algorithm is not only accurate, but also has good robust properties. Moreover, the work shows that utilizing the successive quadratic programming method can also obtain good results provided that the optimization variables are chosen appropriately. Finally, some calculated and measured results are given and discussed to verify the validity of the presented methods further.

Super-resolution reconstruction (SRR) algorithms are used for getting high-resolution (HR) data from low-resolution observations. In Maximum a posteriori (MAP) based SRR the observation model is employed for estimating a HR image that best reproduces the two low-resolution input data sets. The parameters of the prior play a significant role in the MAP based SRR. This work concentrates on the investigation of the influence of one such parameter, called temperature, on the reconstructed 3D MR images. The existing approaches on SRR in 3D MR images use a constant value for this parameter. We use a cooling schedule similar to simulated annealing for computing the value of the temperature parameter at each iteration of the SRR. We have used 3D MR cardiac data sets in our experiments and have shown that the iterative computation of the temperature which resembles simulated annealing delivers better results.

The imperfection of optical devices in an optical imaging system deteriorates wavefront which results in aberration. This reduces the optical signal to noise ratio of the imaging system and the quality of the produced images. Adaptive optics composed of wavefront sensor (WFS) and deformable mirror (DM) is a straightforward solution for this problem. The need for a WFS in an AO system, raises the cost of the overall system, and there are also instances when they cannot be used, such as in microscopy. Moreover stray reflections from lens surfaces affect the performance of the WFS. In this paper, we describe a blind optimization technique with an in-expensive electronics without using the WFS to correct the aberration in order to achieve better quality images. The correction system includes an electromagnetic DM from Imagine, Mirao52d, with 52 actuators which are controlled by particle swarm optimization (PSO) algorithm. The results of the application of simulated annealing (SA), and genetic algorithm (GA) techniques that we have implemented in the sensor-less AO are used for comparison.

In this paper, a novel method based on simulated annealing algorithm (SAA) was described for polarization errors suppression in distributed perturbation sensor based on dual Mach-Zehnder interferometer(DMZI). It was shown both theoretically and experimentally that the inaccuracy (±100m) of the position of the perturbation is caused by polarization-induced phase errors. By applying high speed polarization controller based on SAA, the polarization-induced phase errors were suppressed and the accuracy of the position of the perturbation is up to±10m.

This paper introduces a simulated annealing algorithm for solving the linear constrained optimization problems. The performance of this algorithm is demonstrated with Rosenbrock function global optimal problem. Then the design of cooling schedule and the influence of various controlling parameters are discussed. From the results, it is evident that the quality of the obtained optimal solution is closely related to the iterative times. There are three parameters related to the iterative times, and the most important influence parameter is markov chain.

Markov Random Field (MRF) models have been recently suggested for MRI brain segmentation by a large number of researchers. By employing Markovianity, which represents the local property, MRF models are able to solve a global optimization problem locally. But they still have a heavy computation burden, especially when they use stochastic relaxation schemes such as Simulated Annealing (SA). In this paper, a new 3D-MRF model is put forward to raise the speed of the convergence. Although, search procedure of SA is fairly localized and prevents from exploring the same diversity of solutions, it suffers from several limitations. In comparison, Genetic Algorithm (GA) has a good capability of global researching but it is weak in hill climbing. Our proposed algorithm combines SA and an improved GA (IGA) to optimize the solution which speeds up the computation time. What is more, this proposed algorithm outperforms the traditional 2D-MRF in quality of the solution.

This paper presents a novel method for retrieving different instances of a region or object of interest in a video database. We propose three dynamic region construction and matching algorithms aiming at obtaining the most similar instance of the query model from each candidate image. The first two involve a greedy, dynamic region construction method. The third is based on simulated annealing, and aims at determining a global optimum. An interactive selection mechanism allows the user to select an object directly from the video and to start a query using as input this information in order to access visually similar content. Experimental results show promising performances, with object detection rates of up to 81%.

In the design procedure of system on chip (SoC), it is needed to make use of hardware-software co-design technique owing to the great complexity of SoC. One of main steps in hardware-software co-design is how to carry out the partitioning of a system into hardware and software components. The efficient approaches for hardware-software partitioning can achieve good system performance, which is superior to the techniques that use software only or use hardware only. In this paper, a method based on neural networks is presented for the hardware-software partitioning of system on chip. The discrete Hopfield neural networks corresponding to the problem of hardware-software partitioning is built, the states of neural neurons are able to represent whether the required components or functionalities are to be implemented in hardware or software. An algorithm based on the principle of simulated annealing is designed, which can be used to compute the minimal energy states of neural networks, therefore the optimal partitioning schemes are obtained. The experimental results show that the hardware-software partitioning method proposed in this paper can obtain the near optimal partitioning for a lot of example circuits.

We solve the channel assignment problems (CAPs) with the main objective of minimizing the overall interference level while meeting the channel demand requirements. We use 3 methods, i.e., (1) local search (LS) with an acceptance ratio to re-initialize the search at a predefined threshold; (2) Simulated Annealing (SA); and (3) improve local search (ILS) with two control parameters, namely Restart (RS) and Stop (ST) thresholds. Simulation results on benchmarking CAPs show that these simple methods outperform other more complex heuristics on both the average and minimum cost solutions. Published version

Fabricating defect-free mask blanks remains a major "show-stopper" for adoption of EUV lithography. One promising approach to alleviate this problem is reticle floorplanning with the goal of minimizing the design impact of buried defects. In this work, we propose a simulated annealing based gridded floorplanner for single project reticles that minimizes the design impact of buried defects. Our results show a substantial improvement in mask yield with this approach. For a 40-defect mask, our approach can improve mask yield from 53% to 94%. If additional design information is available, it can be exploited for more accurate yield computation and further improvement in mask yield, up to 99% for a 40-defect mask. These improvements are achieved with a limited area overhead of 0.03% on the exposure field. Defect-aware floorplanning also reduces sensitivity of mask yield to defect dimensions.

The images obtained from confocal imaging systems present less resolution than the theoretical limit due to imperfection of the optical components and their arrangement. This imperfection deteriorates the wavefront and introduces aberrations to the optical system. Adaptive optics (AO) systems composed of a wavefront sensor (WFS) and a deformable mirror represent the most used solution to this problem. Such adaptive optics systems are expensive. In addition, in microscopy, WFSs cannot be used due to stray reflections in the system and high aberrations introduced by the specimen. For these reasons, sensor-less AO systems have been developed to control the deformable mirror (DM) using an optimization algorithm in an iterative manner. At each iteration, the algorithm produces a new set of voltage and sends it to the mirror so as to optimize its shape, in such a way, as to maximize the strength of the photodetector current in the imaging system. In this paper the results of the application of three optimization techniques in the sensor-less AO are compared. The three optimization techniques are simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO). SA and GA have been previously implemented and PSO is explained in this paper.

Laser beam shaping is required in many applications for improving the efficiency of the laser systems. In this paper, the near field beam shaping based on the combination of simulated annealing algorithm and Zernike polynomials is demonstrated. Considering phase distribution can be represented by the expansion of Zernike polynomials, the problem of searching appropriate phase distribution can be changed into a problem of optimizing a vector made up of Zernike coefficients. The feasibility of this method is validated theoretically by translating the Gaussian beam into square quasi-flattop beam in the near field. Finally, the closed control loop system constituted by phase only liquid crystal spatial light modulator and simulated annealing algorithm is used to prove the validity of the technique. The experiment results show that the system can generate laser beam with desired intensity distributions.

One of the major problems in the RET flow is OPC recipe creation. The existence of numerous parameters to tune and the interdependence between them complicates the process of recipe optimization and makes it very tedious. There is usually no standard methodology to choose the initial values for the recipe settings or to determine stable regions of operation. In fact, parameters are usually optimized independently or chosen to resolve a certain issue for a specific design without quantifying its effect on the quality of the recipe or how it might affect other designs. Another problem arises when a quick fix is needed for an old recipe to build new design masks, and this causes the stacking of many customization statements in the OPC recipe, which in turns increases its complexity. Consequently, the experience of the developer is highly required to build a good as well as a stable recipe. In this context, simulated annealing is proposed to optimize OPC recipes. It will be shown how many parameters can be optimized simultaneously and how we can get insight about the stability of the recipe.

The lens constructed with slits perforated on the thin metallic film is designed by simulated annealing algorithm. And two demonstrates are carried out to validate the directional design algorithm. First, the lens with one focal spot is considered. After the width of each slit is obtained, the finite-difference time-domain method is used to check the performance of the designed lens. It is found that the focal length is 800nm and the full-width at half-maximum of the focal spot is 252nm, which corresponds to the preset objective well. Then, the lens with three focal spots is also designed by this algorithm and the performance of the designed lens is quite close to the objective. This is the first work to introduce directional algorithm into design SPP lens. It may pave the way for the design sub-wavelength optic devices.

In this paper GPU based data parallel technique is used to reduce calculating time in diffractive optics elements (DOE) design. By using compute unified device architecture (CUDA) framework, comparison of acceleration radio of some major methods using in DOE design, including Gerchberg Saxton algorithm for global optimization in conjunction with Rayleigh-Sommerfeld diffractive integrating transformation, genetic algorithm with a finite-difference time-domain method and simulated annealing algorithm, is made with demonstrational DOE-lens design, whose focal spot size is 0.5 μm, incident light wavelength is 650 nm, focal length is 3mm, DOE aperture size is 2mm.

This paper presents a basic computational framework for real-time, 3-D light dosimetry on graphics processing unit (GPU) clusters. The GPU-based approach offers a direct solution to overcome the long computation time preventing Monte Carlo simulations from being used in complex optimization problems such as treatment planning, particularly if simulated annealing is employed as the optimization algorithm. The current multi- GPU implementation is validated using a commercial light modelling software (ASAP from Breault Research Organization). It also supports the latest Fermi GPU architecture and features an interactive 3-D visualization interface. The software is available for download at http://code.google.com/p/gpu3d.

Optical coherence tomography (OCT) is an imaging technique widely used in various applications especially in biomedical field. It constructs a high resolution 3-D image using multiple cross-sectional views. The axial resolution can be degraded if the sample is dispersive, which is usually true as most of the samples are living tissues. For time-domain OCT, this dispersion is minimized numerically by introducing a compensation filter, which is applied to the obtained signal in Wigner domain, a time-frequency domain. The filter is designed using simulated annealing optimization technique. This paper shows the design of the filter and the results of dispersion compensation.

A simulated annealing-simplex downhill hybrid algorithm is presented to solve the problems of ellipsometric data inversion. Basing on Monte Carlo technique of simulated annealing algorithm, the hybrid algorithm uses simplex downhill algorithm to get the local optimization and avoids the local optimization to get the global optimization by Metropolis accepting principle, then the global optimum ellipsometric data are obtained quickly. A typical model with single-layer absorbing film was dealt with by the hybrid algorithm and the simulated annealing algorithm respectively in numerical simulation experiments. The results show that the hybrid algorithm is feasible, credible and ascendant in ellipsometric data inversion. Furthermore, with the same testing conditions and inversion precision, the hybrid algorithm can save time with two quantity degrees, so it will be found more applications in practice.

A new global optimization method for thin film thickness calculation is presented in this paper. A new adaptive simulated annealing (ASA) combining with genetic algorithm is brought out. Adding traditional genetic algorithm to simulated annealing process avoids the premature convergence problem and improves the ability of global searching effectively. Firstly it generates a new group of local optimums through genetic and mutation operation; then the solutions are accepted at a certain probability, and will be calculated respectively in ASA process as the initial solutions. At last conjugate gradient algorithm is utilized to search accurately and quickly. This new ASGA can effectively enhance the robustness of the algorithm and reduce the limitation of the searching area. The experimental results show that the proposed algorithm can calculate the thickness of single layer and 2-layer thin film within the range of 10nm to 1μ m and less than 4% calculation error.

Vectorial diffraction methods provide accurate analysis of subwavelength diffractive optical elements, but they usually consume a lot of computing time and computer memory. On the other hand, scalar diffraction methods have simpler physical models and cost much less computer resources at the expense of a large numerical error. Therefore, an approximate method is highly required, which can substantially relieve the computational burden and possess a high accuracy. We firstly propose such an approximate vectorial method, i.e., the improved first Rayleigh-Sommerfeld method (IRSM1), and apply it to analysis of two-dimensional (2-D) cylindrical microlenses and metallic cylindrical focusing micromirrors. The IRSM1 is clearly proved to be much more accurate than the scalar diffraction method. In addition, compared with the vectorial boundary element method, the IRSM1 consumes much less computing time and computer memory. Besides of the above, the IRSM1 is expected to be used in optical designs of subwavelength diffractive optical elements by incorporating with the simulated annealing method or the Yang-Gu algorithm.

Map coloring is a hard task even to the experienced map experts. In the GIS project, usually need to color map according to the customer, which make the work more complex. With the development of GIS, more and more programmers join the project team, which lack the training of cartology, their coloring map are harder to meet the requirements of customer. From the experience, customers with similar background usually have similar tastes for coloring map. So, we developed a GIS color scheme decision-making system which can select color schemes of similar customers from case base for customers to select and adjust. The system is a BS/CS mixed system, the client side use JSP and make it possible for the system developers to go on remote calling of the colors scheme cases in the database server and communicate with customers. Different with general case-based reasoning, even the customers are very similar, their selection may have difference, it is hard to provide a "best" option. So, we select the Simulated Annealing Algorithm (SAA) to arrange the emergence order of different color schemes. Customers can also dynamically adjust certain features colors based on existing case. The result shows that the system can facilitate the communication between the designers and the customers and improve the quality and efficiency of coloring map.

We proposed a new method of generating and optimizing sub-resolution assist features (SRAFs). This method is based on a newly proposed ILT algorithm-Cost-function-Reduction method (CFRM). CFRM is proved to be much effective and efficient than gradient-based algorithm and traditional simulated annealing method. We improve CFRM to be an initial condition independent algorithm (ICIA) by tuning some running parameters. The robustness of ICIA is verified numerically by six mask patterns and two mask technologies in partial-coherence image model using 100 randomly generated mask patterns. Results showed that all are converged to similar final mask patterns with less than 3% differences of the final image edge placement error (EPE). The skeleton of the final mask pattern can be decided by first tens of iterations. Based on the above properties, an efficient and effective algorithm is proposed to handle SRAFs placement. This effectiveness method is demonstrated by different patterns using different mask technologies.

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering associ ation rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system. Keywords : genetic algorithm; simulated annealing algorithm; web-based data mining; Mobile-Learning system.